Publication:
Optimal day-ahead offering strategy for large producers based on market price response learning

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAlcántara Mata, Antonio
dc.contributor.authorRuiz Mora, Carlos
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2022-04-22T15:26:47Z
dc.date.available2022-04-22T15:26:47Z
dc.date.issued2022-04-22
dc.description.abstractIn day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO's market share may imply that her offering strategy can alter the marginalprice formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose a optimization-based methodology to summarize each GENCO's step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the market price and the resulting energy block offering prices is modeled through a Bayesian linear regression approach, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy, represented by the regression coefficient probabilistic distributions. Finally, this predictive model is embedded in the stochastic optimization model by employing a constraint learning approach. Results show how allowing the GENCO to deviate from her true marginal costs renders significant changes in her profits and the market marginal price. Furthermore,these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy is also effective in a real-world market contest.en
dc.identifier.issn2387-0303
dc.identifier.urihttps://hdl.handle.net/10016/34605
dc.identifier.uxxiDT/0000001994es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper Statistics and Econometricsen
dc.relation.ispartofseries22-02
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherStochastic Programmingen
dc.subject.otherConstraint Learningen
dc.subject.otherData-Driven Optimizationen
dc.subject.otherElectricity Marketen
dc.subject.otherOptimal Pricing Strategyen
dc.titleOptimal day-ahead offering strategy for large producers based on market price response learningen
dc.typeworking paper*
dspace.entity.typePublication
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